How do I install Hayhooks?
Run `pip install hayhooks`. For additional features like A2A, Chainlit, or tracing, use `pip install "hayhooks[a2a]"`, `pip install "hayhooks[chainlit]"`, or `pip install "hayhooks[tracing]"` respectively.
@deepset-ai
基本情報
Easily deploy Haystack pipelines as REST APIs and MCP Tools.
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Hayhooks makes it easy to deploy and serve Haystack Pipelines and Agents as REST APIs, and expose them over the MCP (Model Context Protocol), A2A protocol, and OpenAI-compatible endpoints. It is designed for developers who want to integrate Haystack pipelines into AI development environments like Cursor or Claude Desktop with minimal boilerplate.
Install with pip install hayhooks, then run hayhooks run. Create a pipeline wrapper class inheriting from BasePipelineWrapper, deploy it with hayhooks pipeline deploy-files -n <name> <directory>, and call the resulting HTTP endpoints or interact via MCP/A2A clients. Optionally add Chainlit UI with hayhooks run --with-chainlit or tracing with pip install "hayhooks[tracing]".
Run `pip install hayhooks`. For additional features like A2A, Chainlit, or tracing, use `pip install "hayhooks[a2a]"`, `pip install "hayhooks[chainlit]"`, or `pip install "hayhooks[tracing]"` respectively.
Hayhooks supports REST, MCP, A2A, and OpenAI-compatible endpoints. It can expose pipelines as MCP tools, A2A agents, or chat completion backends.
Yes. Install with Chainlit support and run `hayhooks run --with-chainlit` to embed a zero‑configuration chat frontend with streaming, pipeline selection, and custom UI widgets.
Yes, the OpenAI-compatible `/chat/completions` endpoint supports streaming, and the Chainlit UI streams responses natively.
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